Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction

2026-07-09

AI Summary

This work addresses the gap between geometric 3D human pose estimation and the biomechanical attributes required in rehabilitation and sports science. We propose BioModule, a lightweight, pose-estimator-agnostic temporal Transformer module that can be appended to any existing 3D pose estimator to predict biomechanically meaningful quantities from standard 17-joint skeletons. To enable frame-level cross-modal supervision, we construct the first large-scale aligned dataset and systematically analyze the impact of upstream pose accuracy on downstream biomechanical prediction performance. By integrating anatomical coordinate alignment with the Human3.6M family of datasets, BioModule demonstrates consistent effectiveness across seven state-of-the-art pose estimators, enabling, for the first time, non-invasive and physically interpretable visual biomechanical analysis.

Abstract

Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose estimators remain optimized for geometric keypoint accuracy, while many real-world applications in rehabilitation, sports science, ergonomics, and clinical movement analysis require biomechanical quantities that describe how the body moves, loads, and activates. In this work, we propose BioModule, a lightweight plug-in temporal transformer that attaches downstream of any 3D pose estimator and predicts biomechanical attributes from standard 17-joint 3D skeletons. BioModule is estimator-agnostic and requires no modification of the upstream pose model, enabling existing pose estimators to be extended toward physically interpretable motion analysis. To train and evaluate BioModule, we construct a large-scale aligned dataset pairing Human3.6M video and 3D keypoints with the biomechanical label space of Human3.6Mplus. We establish and verify anatomical correspondence between coordinate systems of the two datasets, enabling frame-accurate cross-modal supervision. Using this aligned supervision, BioModule predicts biomechanical quantities. We further benchmark BioModule across seven state-of-the-art 3D pose estimators, providing the first systematic analysis of how upstream pose estimation quality propagates to downstream biomechanical prediction fidelity. The results position BioModule as a compact, modular bridge between vision-based pose estimation and biomechanically meaningful human motion analysis.

Problem

• 3D human pose estimation
• biomechanical attributes
• markerless motion analysis
• human motion analysis
• pose-to-biomechanics

Innovation

• BioModule
• biomechanical attribute prediction
• 3D human pose estimation
• temporal transformer
• cross-modal supervision